A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
ABSTRACT
House Price Index is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from House price prediction to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
INTODUCTION
House price prediction is great project to learn and apply the machine learning algorithm. The basic idea behind this project is we are training the machine using the machine learning algorithm from the data set.
In this busy world it is very difficult to find a house according to our need and budget. It becomes more difficult to find the house in metropolitan cities like Mumbai, Kolkata, Delhi, etc. This project uses the data of Mumbai city in order to train and test the machine so that it become capable of predicting the price of house. Machine learning algorithm makes it easy to know the price of houses depending on the location, area, number of bedrooms, etc.
In this project Random Forest Regression, Linear Regression, and Decision Tree Machine learning algorithm has been used to compare the efficiency of the algorithm. Based on comparison we predict which algorithm best suits for the prediction of price of house in Mumbai.
CONCLUSION AND FUTURE SCOPE
The model designed accuracy depends on the dataset selected, better the dataset better will be the accuracy. Best suited model applied is Random Forest. This can be applied to datset of any city for their house price prediction. The project can be enhanced by UI designing through they can predict the price in more easier and interactive way. In this busy world it will be of immense use to search for a house at near to our workplace.
DATASET LINK
https://www.kaggle.com/
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
ABSTRACT
House Price Index is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from House price prediction to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
INTODUCTION
House price prediction is great project to learn and apply the machine learning algorithm. The basic idea behind this project is we are training the machine using the machine learning algorithm from the data set.
In this busy world it is very difficult to find a house according to our need and budget. It becomes more difficult to find the house in metropolitan cities like Mumbai, Kolkata, Delhi, etc. This project uses the data of Mumbai city in order to train and test the machine so that it become capable of predicting the price of house. Machine learning algorithm makes it easy to know the price of houses depending on the location, area, number of bedrooms, etc.
In this project Random Forest Regression, Linear Regression, and Decision Tree Machine learning algorithm has been used to compare the efficiency of the algorithm. Based on comparison we predict which algorithm best suits for the prediction of price of house in Mumbai.
CONCLUSION AND FUTURE SCOPE
The model designed accuracy depends on the dataset selected, better the dataset better will be the accuracy. Best suited model applied is Random Forest. This can be applied to datset of any city for their house price prediction. The project can be enhanced by UI designing through they can predict the price in more easier and interactive way. In this busy world it will be of immense use to search for a house at near to our workplace.
DATASET LINK
https://www.kaggle.com/
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
This slides present the Statistical foundations in verious machine learning techniques. The intended audiance is Statistics Professionals/ students and Data Scientists.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Std 10 computer chapter 9 Problems and Problem SolvingNuzhat Memon
Std 10 computer chapter 9 Problems and Problem Solving
Problem and Types of problem
Problem solving
Flowchart
Symbols of flowchart
Flowchart to calculate area of rectangle
Flowchart to calculate area and perimeter of circle
Flowchart to compute simple interest
Flowchart to find youngest student amongst two students
Flowchart to find youngest student amongst three students
Flowchart to find youngest student amongst any number of students
Flowchart to find sum of first 50 odd numbers
Flowchart to interchange or swap values of two variables with extra variable
Flowchart to interchange or swap values of two variables without extra variable
Advantage and disadvantage of flowchart
Algorithm
Advantage of flowchart
disadvantage of flowchart
Algorithm
Algorithm to find sum of numbers divisible by 11 in the range of 1 to 100
Algorithm to compute interest
Algorithm to find total weekly pay of employee
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
Intro and maths behind Bayes theorem. Bayes theorem as a classifier. NB algorithm and examples of bayes. Intro to knn algorithm, lazy learning, cosine similarity. Basics of recommendation and filtering methods.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Introduction to python, interpreter vs compiler. Concepts like object oriented programming, functions, lists, control flow etc. Also concept of dictionary and nested lists.
In this slide, variables types, probability theory behind the algorithms and its uses including distribution is explained. Also theorems like bayes theorem is also explained.
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
3. Introduction
Problems faced during buying a house:
1) Buying a house is a stressful thing.
2) Buyers are generally not aware of factors that influence the house
prices.
3) Many problems are faced during buying a house.
4) Hence real estate agents are trusted with the communication
between buyers and sellers as well as laying down a legal contract for
the transfer. This just creates a middle man and increases the cost of
houses.
3
4. They believe that it depends upon:
1) The square foot area
2) Neighbourhood
3) The no. of bedrooms
But it depends upon many factors also…..Such as:
1) No. of storeys.
2) Area outside the house.
3) Rooms on one floor.
4
5. Project Summary
Our project is a machine learning app, based on certain
specifications of your future home it will try to guess the
most accurate price.
Information such as state, city, area, stores.
5
6. Technology Used
1. Machine Learning
Tom Mitchell provides explained: "A computer program is said to learn from
experience E with respect to some class of tasks T and performance measure
P, if its performance at tasks in T, as measured by P, improves with experience
E."
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
6
7. 7
Example: predicting whether the given
object is pen or pencil??
E = the experience of predicting many pens
and pencil.
T = the task of predict pen or pencil
P = the probability that of whether it is a
pen or pencil
8. In general, any machine learning problem can be assigned to one of two broad
classifications:
Supervised learning and Unsupervised learning.
1) Supervised Learning: - In supervised learning, we are given a data set and
already know what our correct output should look like, having the idea
that there is a relationship between the input and the output. Supervised
learning problems are categorized into "regression" and "classification"
problems.
2) In a regression problem, we are trying to predict results within a
continuous output, meaning that we are trying to map input variables to
some continuous function.
8
9. 3) In a classification problem, we are instead trying to predict results in a
discrete output. In other words, we are trying to map input variables into
discrete categories.
• Unsupervised Learning:-Unsupervised learning allows us to approach
problems with little or no idea what our results should look like. We can
derive structure from data where we don't necessarily know the effect of
the variables. We can derive this structure by clustering the data based on
relationships among the variables in the data. With unsupervised learning
there is no feedback based on the prediction results.
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11. HOW IT WORKS ?
COLLECTING DATA : FIRST STEP WAS TO COLLECT
DATA WE COLLECTED DATA FROM DIFFERENT
SOURCES & MERGED THEM TOGETHER TO FORM OUR
TRAINING DATA SET.
THEN WE TRAINED THE MODEL USING MACHINE
LEARNIG ALGORITHM WHICH IN THIS CASE IS
MULTIPLE LINEAR REGRESSION.
BASED ON THE GENERATED GRAPHS WE PREDICT
THE COST OF THE HOUSE
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13. Future Work
Our model had a low rmse score, but there is still room for
improvement. In a real world scenario, we can use such a model to
predict house prices. This model should check for new data, once in a
month, and incorporate them to expand the dataset and produce
better results.
We can try out other dimensionality reduction techniques like
Univariate Feature Selection and Recursive feature elimination in the
initial stages.
We can try out other advanced regression techniques, like Random
Forest and Bayesian Ridge Algorithm, for prediction. Since the data is
highly correlated, we should also try Elastic Net regression technique.
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